SALSA: the stochastic approach for link-structure analysis
ACM Transactions on Information Systems (TOIS)
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Investigating unsupervised learning for text categorization bootstrapping
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
An exact almost optimal algorithm for target set selection in social networks
Proceedings of the 10th ACM conference on Electronic commerce
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Walking in facebook: a case study of unbiased sampling of OSNs
INFOCOM'10 Proceedings of the 29th conference on Information communications
Patterns of temporal variation in online media
Proceedings of the fourth ACM international conference on Web search and data mining
On the hardness and inapproximability of optimization problems on power law graphs
COCOA'10 Proceedings of the 4th international conference on Combinatorial optimization and applications - Volume Part I
Tractable models for information diffusion in social networks
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
A cutting-plane algorithm for solving a weighted influence interdiction problem
Computational Optimization and Applications
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Online social networks have become an imperative channel for extremely fast information propagation and influence. Thus, the problem of finding a minimum number of seed users who can eventually influence as many users in the network as possible has become one of the central research topics recently. Unfortunately, most of related works have only focused on the network topologies and largely ignored many other important factors such as the users' engagements and the negative or positive impacts between users. More challengingly, the behavior of information propagation across multiple networks simultaneously remains an untrodden area and becomes an urgent need. Our work is the first attempt to tackle the above problem in multiple networks, considering these lacking important factors. In order to capture the users' engagement, we propose to targeting the set of interest-matching users whose interests are similar to what we try to propagate. Then, we develop our Iterative Semi-Supervising Learning based approach to identify the minimum seed users. We validate the effectiveness of our solution by using real-world Twitter-Foursquare networks and academic collaboration multiple networks.